1 citations · 2 across the 6 of their papers we have counts for
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Inverse Autoregressive Flows for Zero Degree Calorimeter fast simulation
Emilia Majerz, Witold Dzwinel, Jacek Kitowski
Physics-based machine learning blends traditional science with modern data-driven techniques. Rather than relying exclusively on empirical data or predefined equations, this method…
Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Evaluation of Forward-Only Algorithms
Przemysław Spyra, Witold Dzwinel
The long-held assumption that backpropagation (BP) is essential for state-of-the-art performance is challenged by this work. We present rigorous, hardware-validated evidence that t…
SuperNet -- An efficient method of neural networks ensembling
Ludwik Bukowski, Witold Dzwinel
The main flaw of neural network ensembling is that it is exceptionally demanding computationally, especially, if the individual sub-models are large neural networks, which must be…
2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements
Witold Dzwinel, Rafal Wcislo, Stan Matwin
In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool…